1. Field of the Invention
The present invention relates generally to business assessment tools, and more particularly, but not by way of limitation, to a method and system for assessing and planning business operations utilizing rule-based statistical modeling.
2. Description of the Related Art
Statistical analysis has long been used to analyze past events and predict future trends based on the past events. Statisticians often develop statistical models that are used in performing the statistical analysis. Developing and applying such statistical models is technically difficult, requires a great deal of understanding, and is often a trial and error process. Furthermore, interpretation of the results to determine validity of the statistical models requires significant conceptual analysis and expertise.
Statistical analysis may be utilized by businesses that are interested in improving their ability to assess their current fiscal state and predict future activities. For example, businesses that produce consumer goods are interested in determining production and inventory requirements to meet future market demands. Data representative of past events, such as sales, advertising efforts, pricing, etc., may be utilized by the statistician in developing the statistical models.
Since the development of computers, software tools have become a significant asset for statisticians in developing statistical models for businesses. However, these software tools are limited in functionality and intelligent features. Some software tools provide time-series analysis capabilities, but do not support regression analysis. High-end spreadsheet programs provide statistical analysis functions, such as regression and analysis of variance, but rely on an operator to (i) understand the statistical analysis functions, (ii) know how to apply the functions, and (iii) interpret results of the statistical analysis functions. For example, in the case of regression analysis, the operator needs to evaluate a resulting correlation coefficient, and determine whether the correlation coefficient indicates that a relationship between two parameters (e.g., sales and pricing) is strong enough to warrant use of a regression model in further analyses. In a case of performing analysis of variance, once a statistical tool has calculated the F-value for the analysis of variance (the quotient of the total sum of squares divided by the error sum of squares), the operator is required to compare the F-value to a corresponding value accessed from a table of statistics. This comparison provides an assessment of the significance of the variance and serves as the indication that the variance is either due to chance or some other factor that must be determined through further analysis.
From the above examples, it should be understood that statistical analysis is non-trivial. Initially, the statistician must first determine useful parameters or historical data for assessing and predicting a future trend. Secondly, the statistician must utilize multiple statistical tools and understand how to apply the statistical tools to develop a statistical model. Thirdly, the statistician must interpret results produced by the statistical tools to determine whether the developed statistical model is valid. Fourthly, if the statistical model is invalid, then the statistician must determine whether alternative statistical models can be utilized, where each of the alternative statistical models have different results that may have to be analyzed in different ways to determine if the alternative model(s) are valid. Because of the complexity involved in producing a valid statistical model, in general, only highly skilled statisticians are capable of performing such work.